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Cited by 1,490

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

A survey on deep learning techniques for image and video semantic segmentation

Garcia-Garcia, Alberto, Orts-Escolano, Sergio, Oprea, Sergiu, Villena-Martinez, Victor, Martinez-Gonzalez, Pablo, Garcia-Rodriguez, Jose·2018·Applied Soft Computing
DOI 10.1016/j.asoc.2018.05.018
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Abstract

Abstract Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we formulate the semantic segmentation problem and define the terminology of this field as well as interesting background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review common loss functions and error metrics for this problem. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.

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Show all 42 references

Research works printed but not linked: 34

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Metadata

Authors
Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P., & Garcia-Rodriguez, J.
Venue
Applied Soft Computing
Publisher
Elsevier BV
Published
2018-09
Volume
70
Pages
41-65
ISSN
1568-4946 (ISSN-L)
DOI
10.1016/j.asoc.2018.05.018
OpenAlex
W2804860796
Cited by
1,490
References
42
FWCI
69.54
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
Universidad de Alicante

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CoChat. (2026). Citation check: A survey on deep learning techniques for image and video semantic segmentation. https://app.cochat.ai/papers/10.1016/j.asoc.2018.05.018
The paper
Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P., & Garcia-Rodriguez, J. (2018). A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2018.05.018

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